Allocation

Abstract

Thyroid disrupting chemicals (TDCs) are a variety of xenobiotics agents that can potentially interferes with synthesis, secretion, transport, metabolism, binding action, or elimination of natural thyroid hormones (THs). Exposures to TDCs could induce severe disorders in the vital physiological processes in human and wildlife, including homeostasis, macronutrient metabolism, energy balance, brain development, and reproduction. Transthyretin (TTR), a serum TH transporter, and thyroid receptor (TR) have been reported as molecular targets of TDCs, including per and polyfluoroalkyl substances (PFASs) and hydroxylated polychlorinated biphenyls (OH-PCBs). Despites the decades of efforts, design of new compound candidates as replacements for the TDCs remains a challenging scientific topic.
Deep generative models, e.g. generative adversarial networks (GANs), are the emerging deep learning and artificial intelligence methods to design new compound structures under predefined physicochemical properties and biological activities. The methods have been implemented in designing drug molecules against Malaria (ACS Cent. Sci. 2018, 4, 120−131) and electroluminescent molecules of organic light-emitting diode (Nat. Mater. 15,1120–1127, 2016).
In this pilot study, we are aimed at developing deep generative models based on the experimental TTR and TR activities data from our previous studies (Environ. Sci. Technol. 49, 16, 10099-10107; Chem. Res. Toxicol. 29, 8, 1345-1354; Environ. Sci. Technol. 50, 21, 11984-11993) and US NIH Tox21 HTS TR assays to in silico design new compounds that have no biological activities against TTR and TR. The thyroid activities newly generated “non-toxic” compounds will be tested in the in vitro bioassays to validate the model performances. For the unsuccessful “toxic” compound generated, we will investigate their molecular interactions with the targets using molecular docking and molecular dynamics (MD) simulations to understand the important interactions for compound-induced thyroid toxicities and summarize the structure-activity relationship (SAR) of both “toxic” and “non-toxic” compounds.
The outcomes of this study will provide us 1) deep generative models that can in silico generate compound candidates without thyroid disrupting activities; 2) a list of potential “non-toxic” compounds that could be used as potential candidates for “non-toxic” function material design; 3) summarized SARs of thyroid disrupting (“toxic”) compounds and non-thyroid disrupting (“non-toxic”) compounds. More importantly, this study will initiate our endeavors in applying state-of-art of deep generative models and artificial intelligence technologies in in silico design of “non-toxic” compounds, which lays the foundations of in silico design of “non-toxic” functional materials and will lead us to be a step farther on our way to “non-toxic” substance world.